code: def processing_config(
      processor,
      inputs=None,
      outputs=None,
      job_name=None,
      experiment_config=None,
      container_arguments=None,
      container_entrypoint=None,
      kms_key_id=None,
  ):
      """Export Airflow processing config from a SageMaker processor
  
      Args:
          processor (sagemaker.processor.Processor): The SageMaker
              processor to export Airflow config from.
          inputs (list[:class:`~sagemaker.processing.ProcessingInput`]): Input files for
                  the processing job. These must be provided as
                  :class:`~sagemaker.processing.ProcessingInput` objects (default: None).
          outputs (list[:class:`~sagemaker.processing.ProcessingOutput`]): Outputs for
              the processing job. These can be specified as either path strings or
              :class:`~sagemaker.processing.ProcessingOutput` objects (default: None).
          job_name (str): Processing job name. If not specified, the processor generates
              a default job name, based on the base job name and current timestamp.
              experiment_config (dict[str, str]): Experiment management configuration.
                  Optionally, the dict can contain three keys:
                  'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.
                  The behavior of setting these keys is as follows:
                  * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
                  automatically created and the job's Trial Component associated with the Trial.
                  * If `TrialName` is supplied and the Trial already exists the job's Trial Component
                  will be associated with the Trial.
                  * If both `ExperimentName` and `TrialName` are not supplied the trial component
                  will be unassociated.
                  * `TrialComponentDisplayName` is used for display in Studio.
          container_arguments ([str]): The arguments for a container used to run a processing job.
          container_entrypoint ([str]): The entrypoint for a container used to run a processing job.
          kms_key_id (str): The AWS Key Management Service (AWS KMS) key that Amazon SageMaker
              uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key,
              ARN of a KMS key, alias of a KMS key, or alias of a KMS key.
              The KmsKeyId is applied to all outputs.
  
      Returns:
          dict: Processing config that can be directly used by
              SageMakerProcessingOperator in Airflow.
      """
      if job_name is not None:
          processor._current_job_name = job_name
      else:
          base_name = processor.base_job_name
          processor._current_job_name = (
              utils.name_from_base(base_name)
              if base_name is not None
              else utils.base_name_from_image(
                  processor.image_uri, Processor.JOB_CLASS_NAME
              )
          )
  
      config = {
          "ProcessingJobName": processor._current_job_name,
          "ProcessingInputs": input_output_list_converter(inputs),
      }
  
      processing_output_config = (
          sagemaker.processing.ProcessingJob.prepare_output_config(
              kms_key_id, input_output_list_converter(outputs)
          )
      )
  
      config["ProcessingOutputConfig"] = processing_output_config
  
      if experiment_config is not None:
          config["ExperimentConfig"] = experiment_config
  
      app_specification = (
          sagemaker.processing.ProcessingJob.prepare_app_specification(
              container_arguments,
              container_entrypoint,
              processor.image_uri,
          )
      )
      config["AppSpecification"] = app_specification
  
      config["RoleArn"] = processor.role
  
      if processor.env is not None:
          config["Environment"] = processor.env
  
      if processor.network_config is not None:
          config[
              "NetworkConfig"
          ] = processor.network_config._to_request_dict()
  
      processing_resources = sagemaker.processing.ProcessingJob.prepare_processing_resources(
          instance_count=processor.instance_count,
          instance_type=processor.instance_type,
          volume_kms_key_id=processor.volume_kms_key,
          volume_size_in_gb=processor.volume_size_in_gb,
      )
      config["ProcessingResources"] = processing_resources
  
      if processor.max_runtime_in_seconds is not None:
          stopping_condition = sagemaker.processing.ProcessingJob.prepare_stopping_condition(
              processor.max_runtime_in_seconds
          )
          config["StoppingCondition"] = stopping_condition
  
      if processor.tags is not None:
          config["Tags"] = processor.tags
  
      return config
  
imports: from __future__ import print_function, absolute_import
  import os
  import re
  import sagemaker
  from sagemaker import fw_utils, job, utils, s3, session, vpc_utils
  from sagemaker.amazon import amazon_estimator
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.estimator import EstimatorBase
  from sagemaker.processing import Processor
  from sagemaker.utils import format_tags
label_desc: Use image node_id 19 for calling a global function with example usage: processing_config(processor, inputs, outputs, job_name, experiment_config, container_arguments, container_entrypoint, kms_key_id) and returns: config